- Tokenization in Natural Language Processing is where we breakdown text into smaller ,meaningful units called tokens. It is about taking a string and breaking it into chunks.
- Read this article on Natural Language Processing to kick start
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Word Tokenization:
- Breaks text into individual words.
- Example: "The quick brown fox jumps over the lazy dog." becomes ["The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"]
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Character Tokenization:
- Breaks text into individual characters.
- Example: "Hello" becomes ["H", "e", "l", "l", "o"]
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Subword Tokenization:
- Breaks text into subword units, such as prefixes, suffixes, or word stems.
- Example: "running" might be tokenized as ["run", "##ing"]
- Machine Readability: Tokenization allows computers to "understand" text by converting it into a numerical representation that machine learning models can work with.
- Feature Extraction: Tokenization helps extract meaningful features from text, such as word frequency, word order, and sentence structure, which are crucial for various NLP tasks.
- Contextual Understanding: Tokenization helps identify the context of words by analyzing their surrounding tokens. This is important for tasks like sentiment analysis and text classification.
Tokenization in Practice:
- Search Engines: Breaking down text into tokens helps search engines index and retrieve relevant information.
- Sentiment Analysis: Analyzing the sentiment of text requires understanding the meaning of individual words and their relationships.
- Machine Translation: Tokenization is essential for breaking down text into smaller units that can be translated independently.
- Text Summarization: Identifying the most important information in a text often involves understanding the meaning of individual words and their relationships.